mcpbeat

Hephaestus Build

agentlas-ai/hephaestus-build

Use when the user types /prompts:hep-build, mentions @Hephaestus for build work, asks to create a single Agentlas agent, create a multi-agent team, or package an existing local/external agent into Agentlas architecture.

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
1165
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/agentlas-ai/Agentlas-OS --skill hephaestus-build

The instruction itself

3 sections, as written by the author

Hephaestus Build

Procedure

  • Treat this as the public Codex build surface. Do not expose or ask the user

to invoke the older internal support skill names.

  • Read AGENTS.md and .agentlas/mode-map.json when they exist in the

current workspace.

  • Run the public mode classifier by independent ownership boundaries, not by

keywords such as "team":

  • package or repair existing material -> 30-agentlas-packager;
  • one independently owned context/tools/success standard ->

10-single-agent-builder;

  • two or more roles with separate context, permissions, success standards,

handoff, or synthesis needs -> 20-multi-agent-team-builder.

If the shape is unclear, ask before generating. The user-facing question

must be plain language, for example: "이 일을 한 명의 전문가가 처음부터 끝까지

맡으면 되나요, 아니면 조사/분석/검토처럼 여러 전문가가 나눠 맡고 마지막에

합쳐야 하나요?" Do not expose internal labels such as single-agent,

team-builder, ownership boundary, memory/context, synthesis, or

produces/consumes.

  • Run the Builder Interview and Research Gate from

docs/builder-interview-research-gate.md before writing substantial package

files:

  • ask an 8-12 question first batch when the request is vague;
  • continue follow-ups until target user, tasks, inputs, outputs, examples,

role count, separated tools or permissions, final merge needs, execution

order, memory, failure modes, and evals are clear;

  • phrase shape questions in everyday language. Ask who handles which part,

whether each role needs different files/accounts/tools, whether someone

must merge the result, and whether work can run at the same time or must

pass from one person to the next;

  • research official or primary docs, similar agent repositories or

comparables, GitHub examples, academic/professional theory, and

tool/plugin docs;

  • compare selected and rejected tools/plugins with permission, secret,

fallback, and smoke-test notes;

  • synthesize domain-expert behavior from interview answers, comparable

agents/repos, theory, and tool choices;

  • write docs/builder-interview.md, docs/research-sources.md,

docs/tool-selection.md, docs/domain-expert-synthesis.md,

docs/prompt-performance-contract.md, and

.agentlas/capability-eval-plan.json.

  • If missing narrow details still change files, adapters, or public/private

boundaries, ask one to five clarify questions before generating.

  • Pick one:
  • 10-single-agent-builder;
  • 20-multi-agent-team-builder;
  • 30-agentlas-packager.
  • Load matching support skills.
  • Write all generated or repaired runtime agent instructions in English:

AGENTS.md, CLAUDE.md, GEMINI.md, agent.md, skills, workflow/command

adapters, runtime prompts, handoff contracts, return contracts, and

operating docs. Translate Korean or other-language source material into

English agent behavior. Localized public copy, routing trigger examples, and

sample user inputs may use the target user language.

  • Emit or repair Agentlas contracts, including .agentlas activation seed

files and .agentlas/global-commands.json when local continuity is part of

the output.

10. Add the generated command to Claude Code, Codex, Gemini CLI, generic

AGENTS.md, and terminal adapters. For teams, expose the orchestrator/HQ

command and route workers through HQ unless direct worker commands were

requested.

11. Run scripts/verify-team-package.sh <generated-package-root> for generated

or repaired packages. If it fails, do not report completion; collapse the

output to a single-agent package or add the required orchestrator/HQ and

team contracts.

12. Verify with scripts/verify-package.sh.

13. Once verification and local registration have succeeded, ask one final

two-choice storage question using structured controls when available:

Cloud에 올리기 or 로컬에만 저장. Cloud means owner-private Agent

Cloud storage, restorable on another signed-in Desktop. Mobile can use the

package only after a paired Desktop restores/installs it; Cloud is not a

hosted LLM runtime. Local-only performs no network mutation.

14. Never upload by default. Missing input and non-interactive execution are

local-only. Only after explicit Cloud consent, run the trusted Hephaestus

runner with `upload <exact-verified-package-root> --visibility

private-link`. Keep the local package on every auth/offline/CAS/quota/scan

failure and report an exact retry command. Public Hub publication remains

a separate explicit action.

Output

Return status, evidence, output, global_commands, interview_research,

and blockers.

How to use it

Copy the folder

Take agentlas-ai/hephaestus-build from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.